Enseigné par

Christian Terwiesch

Transcription

[MUSIC] So now, just a recap. We have done two studies to show us some very interesting findings about spreading, contagion, and influential users. We have looked at demand for net grosser.com spreading through neighborhoods throughout the United States. We have also looked at who influential for whom on social networking sites. So were have been in the real world and the virtual world. Now lets flip back to the real world again. So one area that gets looked at a lot for influence and contagion is among professional, particularly among professionals in the medical community. So what I'm showing here is a diagram that's coming from a paper that was written by two of my colleagues here at the Wharton school and also one of their colleagues at the University of Southern California. This is a network you might recognise this kind of diagram from the very first things that we wrote when we started this discussion about networks and neighborhoods. YouTube link that I gave you to show the spreading of obesity, that controversial study, throughout Boston in the United States. This is the same thing here. What this is showing is among 174 positions in the Los Angeles area, who is connected to whom, and the size of the circle is also indicating some degree of influence. So what the firm, the medical firm and the researchers wanted to understand is who in this network is influential for whom and by how much? So again to go back to my friend Chris who's now going to wear the hat of a Doctor. Chris is a A physician in LA, and I'm also a physician. If he starts prescribing a certain drug test patients, maybe I'm going to follow on and do the same thing. And of course if the drug company knows which stock is the influential, that's very important for targeting purposes. So what the colleagues did is they use the methodology very, very similar to what I showed you earlier. Maybe we had the force of codes, C1, C2, C3, C4. In this case, it's the same idea but instead of zip codes, they're doctors. So they looked at which doctors were connected to whom. That's the first piece of information. And then secondly, which doctors had already prescribed the drug, and which doctors had yet to do so. So remember when we study contagion processes, we need to know two things who's connected to whom, and who's done what up until the current point in time. Now, they found a couple of really interesting things. Let me give you the highlights. They measured influence and contagion in two very different ways. One way was to just ask people on a self reported basis. Hey, are you influential, on a scale of 1 to10. And it turns out, if people say they're influential, it's not too bad, but it's actually a relatively weak predictor compared to an indirect measure of measuring influence, which is whether or not I'm citing Chris's work as a doctor. I'm referring to a scientific study. So, instead of looking at a measure of influence that is self reported, what they did is they had another measure of influence that was in some sense more objective, where doctors were referring to each others' work and each others' scientific studies. And they found the second one was more important in predicting the way these drugs were going to. So now let me give you the main takeaways from the study which I think is really really fascinating. First of all, the firm found it was really helpful for them to try and understand the network structure of their customers. In this case, the doctors. And also an understanding of network structure they were able to identify the contagion process was at work and the contagion process was driven by these influential people. Now what's really interesting about this is some of the interest, the influential people weren't necessarily the people who put their hands up and said, hi I'm influential. But they were the people that they figured out indirectly were influential that is the doctors to whom others referred in terms of scientific studies and citations and so on. And there were also some quite special people who had their feet in different camps as it were. So in the study they found that there were certain Asian American doctors who both were influential for other Asian Americans but also for other people outside of their ethnic group as well. So very, very interesting. It tells us that understanding the network structure is important. Number two that contagion occurs through the network. And number three in all networks there are certain special people who are more influential than others and our job as marketeers is to try and understand who those are. So now let's turn to our fourth and final study. We've just finished the study of physicians in the real world, we've looked at some other things in the virtual world and now we're going to go back to the virtual world again but with a real world twist. The company that we looked at in this case is a company called Bonobos that's been around since about 2007 selling men's clothing online also, selling through traditional retailers, and I think I mentioned them a little bit earlier in the piece as well. So this resulted in a paper that my colleague and friend Jae Young Lee and I wrote about something called neighborhood social capital and online sales. So let's look in and see what that's talking about. So now I'm just showing you a screen shot of the company, Bonobos. So you can see that they're selling to men. The target is males age roughly 20 to 45 who are somewhat fashion forward and looking for affordable, fashionable clothing. So, what we wanted to do in this case. Is we wanted to try and understands whether or not there was real world interaction that was increasing the virtual world sales of this company. So what I mean by this that my friend Chris and I. That Chris is back at the picture this time. It's just a regular friend who want to pair friends. He is not a doctor anymore. So Chris and I are friends. And if Chris happens to buy some items of clothing from Demoboza.com and I see him wearing them and he tells me about them. Is that going to lead me to then increase the chance that I buy it from the same website? That's what we wanted to look at. Whether or not interaction in the real world is going to lead to additional sales. In the virtual world or at the website of the company. That was the first piece, now the second piece again here it's on the slide. We wanted to see whether or not there was an effect of something called social capital. Social capital is a really fascinating concept and it's one that was really coined I believe by a fellow at the Harvard University's Kennedy School of Government, a gentleman by the name of Robert Putnam. I wrote a book called "Bowling Alone". You think of then pin bowling in America. The metaphor is bowling alone that for people that are less social than they used to be. Maybe they were spending all of their time online disconnected from other people and so what he wanted us to do was he wanted to understand and local neighborhoods how connected people were to others that they participate in churches, in tennis clubs and get together with each other and so on, and what he did is he did a huge survey called the Social Capital Community Benchmark Survey when he and his team literally went around about 30,000 different zip codes within the United States. And they asked people like my friend Chris, hey Chris do you like your neighbors? Do you trust your neighbors? Do you interact with your neighbors? And so some very interesting data was collected about trust and interaction. So neighborhoods with more trust and interaction are neighborhoods that have higher social capital. We wanted to see whether neighborhoods with more social capital that would be more sharing, more efficient sharing of information. Now I just want to draw your attention to one other thing about this particular website. There are three conditions related to the product that are particularly important for our study. The first is that items of clothing like the sweater I'm wearing have what are called non digital attributes. What does that mean? Well a non digital attribute that is very hard to represent perfectly over the internet. So price is a digital attribute, if I go to Amazon and I see a book that's costing $20. I know it's $20, it's easy to communicate price information over the Internet just as it would be if it would positioned in a store. However, to try and communicate how this fits and feels is actually quite difficult. So in that case transmission of information form one customer to another in the real offline environment could be very very important. Secondly in our study, we wanted to focus on those customers who hadn't yet bought anything from the website, that this was going to be their first time purchase, why is that? Well because, once you've bought something from a website, and you've tried on the sweater, you have your own judgement, you don't necessarily need the opinion of other people, unless it's about the overall fashionability. And then finally this is a product that's socially visible, so it might be one that actually generates a conversation. I might see Chris and say, hey Chris, you look very well dressed today, where'd you get those pants? And then a conversation ensues. I just want to reiterate that this is a product category that's a little bit different to most of the products that we've been talking about that are being sold by our friends at Quidsi. Places like Soap.com and Diapers.com, those products have primarily digital attributes. There's no real surprise if you order some Tide detergent and it shows up at your house. You know exactly, what you're going to get. There's no problem communicating that through the internet. So, this time we wanted to look at a business that was a little bit different, that was a fashion business that had these other properties. So let me show you what the raw data looked like. These are just the sales data for the company over the first. Think 42 months or three and a half years of operation. You can see that over time, the number of new customers coming in is going up. You can also see with the blue arrows that the neighborhoods with as more trusting interaction, the sales are higher than the neighborhoods were there's less trusting interaction. So what if this will mean? Well, Jane and I put together a statistical model to try and understand this in more detail. And what do we find? And the findings that are here shown on the screen. But let me explain what's going on here. We found that of about the 6,000 trials that we looked at, at least half of them were influenced by what we call social learning, meaning the evidence from the statistical analysis suggested that some of these new customers became customers because someone in their local neighborhood told the, told them about it. So that's a pretty important effect, half of all the sales of this company. Secondly, we found that the customers who came later on were the ones that were most influenced by the social interaction. That ties back to some of the things that we talked about earlier. The people who do things right in the beginning, they do not usually need to rely so much on the opinions of others. They just like to go out and do stuff but people who come in later they require more social information typically. And that was also confirmed in our study. The second thing that we found that was really, I think, the most interesting finding to us was the following. In neighborhoods where there's more trust and interaction, more social capital, there's not necessarily sales. So it's not that just neighborhoods with trust and interaction have people who buy more stuff. But what happens is in those neighborhoods when information gets transmitted, it's more believable and it's more trustworthy and it's more efficient. So if Chris and I life in a neighborhood where we trust each other and like each other, if he tells me something I put more weight on it. That's the result that was coming through here. So how could the firm Bonobos.com or any firm kind of use this information? Well when we did our analysis, we were a little bit restricted to only the zip codes where the social capital survey measures had been collected. So there were many many zip codes in the United States for which those measures were not collected. Now earlier I think I said that Mr. Putnam went out and he measured 30,000 zip codes. Actually just to be clear, he measured 30,000 people. Who were living in about 1,000 zip codes. So if a firm really wanted to use this, clearly only knowing about 1,000 zip codes is not quite enough. So here's a question I want to put to you all out there. And then I'll give you the answer. If you can think of a proxy, that means some other variable, other than the true measure of social capital. That would indicate that males aged roughly 20 to 45 were socializing together and had some level of social capital, what might it be? The number of hospitals per zip code, number of churches maybe, number of rugby clubs okay, you're going in the right direction. Turned out that the number of bars and liquor stores per capita was a very nice predictor of the efficient diffusion of information among this group. Why am I telling you this? Because I want you to be creative and to think a little bit expansively when you start to use these concepts and you start to think about gee how could I use this idea for my own business that I'm working on or the company that I'm working at now. So that brings the conclusion to this piece of our discussion. I hope you enjoyed those four studies, number netgoso.com number two the social networking side of influence, number three looking at the fusion of drug prescribing behavior by physicians and finally how offline interaction is affecting people sales sales of product on the internet for Bonobos.com. [MUSIC]